Learn Deep Learning from these Books

Deep Learning is the only comprehensive book on the subject. - Elon Musk, cochair of OpenAI co-founder and CEO of Tesla and SpaceXDeep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human-computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones a graph of these hierarchies would be many layers deep.

This book has been prescribed and recommended as a textbook in the latest AICTE Model Curriculum of Engineering & Technology 2018-19.

A good book is like a teacher who sits behind the reader and guides him/her accordingly. - Deep Learning has been an area of current research. - After toiling through the various concepts of Deep Learning, the book slithers around all principles of deep learning. This book highlights in deep the concepts of deep learning so that new projects and researchers can be done. - The book serves, both as textbook and as a reference book. Some of the highlights of the book are: Simple Language, Recent Concepts of Machine and Deep Learning explained, MCQ's, Conceptual Short Questions & Answers, Case Studies, Case Tools(like Tensor - Flow, H2O etc)

Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human-computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.

Deep learning is applicable to a widening range of artificial intelligence problems, such as image classification, speech recognition, text classification, question answering, text-to-speech, and optical character recognition. Deep Learning with Python is structured around a series of practical code examples that illustrate each new concept introduced and demonstrate best practices. By the time you reach the end of this book, you will have become a Keras expert and will be able to apply deep learning in your own projects. KEY FEATURES Practical code examples In-depth introduction to Keras Teaches the difference between Deep Learning and AI.